Large language models (LLMs) hold transformative potential for medical image labeling in radiology, addressing challenges posed by linguistic variability in reports. We developed a two-stage natural language processing pipeline that combines Bidirectional Encoder Representations from Transformers (BERT) and an LLM to analyze radiology reports. In the first stage (Entity Key Classification), BERT model identifies and classifies clinically relevant entities mentioned in the text. In the second stage (Relationship Mapping), the extracted entities are incorporated into the LLM to infer relationships between entity pairs, considering actual presence of entity. The pipeline targets lesion-location mapping in chest CT and diagnosis-episode mapping in brain MRI, both of which are clinically important for structuring radiologic findings and capturing temporal patterns of disease progression. Using over 400,000 reports from Seoul Asan Medical Center, our pipeline achieved a macro F1-score of 77.39 for chest CT and 70.58 for brain MRI. These results highlight the effectiveness of integrating BERT with an LLM to enhance diagnostic accuracy in radiology report analysis.